Embedding Geographic Locations for Modelling the Natural Environment using Flickr Tags and Structured Data

TL;DR

This paper introduces a method for learning vector space embeddings of geographic locations by integrating Flickr photo tags and structured data, improving ecological modeling and prediction accuracy.

Contribution

It presents a novel embedding approach that combines unstructured Flickr tags with structured geographic data, enhancing ecological feature modeling.

Findings

Embedding method outperforms existing approaches

Improved modeling when structured data is available

Enhances ecological prediction accuracy

Abstract

Meta-data from photo-sharing websites such as Flickr can be used to obtain rich bag-of-words descriptions of geographic locations, which have proven valuable, among others, for modelling and predicting ecological features. One important insight from previous work is that the descriptions obtained from Flickr tend to be complementary to the structured information that is available from traditional scientific resources. To better integrate these two diverse sources of information, in this paper we consider a method for learning vector space embeddings of geographic locations. We show experimentally that this method improves on existing approaches, especially in cases where structured information is available.